The question of representation has long been central to neuroscience, beginning perhaps with the work of Hubel and Weisel [1]} and continuing robustly to the present day [2]}, [3]}, [4]}. Neuroscientific studies of representation have benefited from tools made available by deep learning [5]}, [6]}, which provides models of how representations can be shaped by sensory experience. Deep RL expands this toolkit, providing for the first time models of how representations can be shaped by rewards and by task demands. In a deep RL agent, reward-based learning shapes internal representations, and these representations in turn support reward-based decision making. A canonical example would be the DQN network training on an Atari task. Here, reward signals generated based on how many points are scored feed into a backpropagation algorithm that modifies weights throughout the deep neural network, updating the response profiles of all units. This results in representations that are appropriate for the task. Whereas a supervised learning system assigns similar representations to images with similar labels (Figure  REF a,b), deep RL tends to associate images with similar functional task implications (Figure  REF c,d).
